Hadoop Framework Guide

Hadoop Framework Guide

Introduction

Hadoop Framework - Components, and Uses

If you are learning about Big Data, you are bound to come across mentions of the "Hadoop Framework". The rise of big data and its analytics have made the Hadoop framework very popular. Hadoop is open-source software, meaning the bare software is easily available for free and customizable according to individual needs.

This helps in curating the software according to the specific needs of the big data that needs to be handled. As we know, big data is a term used to refer to the huge volume of data that cannot be stored or processed, or analyzed using the mechanisms traditionally used. It is due to several characteristics of big data. This is because big data has a high volume, is generated at great speed, and the data comes in many varieties.

Since the traditional frameworks are ineffective in handling big data, new techniques had to be developed to combat it. This is where the Hadoop framework comes in. The Hadoop framework is primarily based on Java and is used to deal with big data.

 

What is Hadoop?

Hadoop is a data handling framework written primarily in Java, with some secondary code in shell script and C. It uses a basic-level programming model and is able to deal with large datasets. It was developed by Doug Cutting and Mike Cafarella. This framework uses distributed storage and parallel processing to store and manage big data. It is one of the most widely used pieces of big data software.

Hadoop consists mainly of three components: Hadoop HDFS, Hadoop MapReduce, and Hadoop YARN. These components come together to handle big data effectively. These components are also known as Hadoop modules.

Hadoop is slowly becoming a mandatory skill required from a data scientist. Companies looking to invest in Big Data technology are increasingly giving more importance to Hadoop, making it a valuable skill upgrade for professionals. Hadoop 3.x is the latest version of Hadoop.

 

How Does Hadoop Work?          

Hadoop's concept is rather straightforward. The volume, variety, and velocity of big data offer problems. Building servers with heavy setups that could handle such a vast data pool at ever-increasing sizes would not be viable. It would be simpler to connect numerous computers using a single CPU as an alternative, though.

This would turn it into a distributed system that works as one system. This indicates that the clustered computers can work together in parallel to achieve the same objective. This would expedite and reduce the cost of handling large amounts of data.

This can be better understood with the help of an example. Imagine a carpenter who primarily makes chairs and stores them at his warehouse before they are sold. At some point, the market demands other products like tables and cupboards. So now the same carpenter is working on all three products. However, this is depleting his energy, and he is not able to keep up with producing all three.

He decides to enlist the help of two other apprentices, who each work on one product. Now they are able to produce at a good rate, but a problem regarding storage arises. Now the carpenter cannot buy a bigger and bigger warehouse as per increases in demand or product. Instead, he takes three smaller storage units for the three different products.

The carpenter in this analogy might be compared to the server that manages data. Big data, which is too much for the server to handle alone, is created by the increase in demand, which is expressed in the variety, velocity, and volume of the product.

Now that he has two apprentices reporting to him, they are all working towards the same objective thanks to the concept of a single CPU assisted by many computers. Storage is assigned to curated storage based on variety to prevent a bottleneck. This is essentially how Hadoop functions.

 

Main Components of Hadoop Framework

hadoop framework 1

There are three core components of Hadoop as mentioned earlier. They are HDFS, MapReduce, and YARN. These together form the Hadoop framework architecture.

  • HDFS (Hadoop Distributed File System):

It is a data storage system. Since the data sets are huge, it uses a distributed system to store this data. It is stored in blocks where each block is 128 MB. It consists of NameNode and DataNode. There can only be one NameNode but multiple DataNodes.

Features:

  • The storage is distributed to handle a large data pool
  • Distribution increases data security
  • It is fault-tolerant, other blocks can pick up the failure of one block
  • MapReduce:

The MapReduce framework is the processing unit. All data is distributed and processed parallelly. There is a MasterNode that distributes data amongst SlaveNodes. The SlaveNodes do the processing and send it back to the MasterNode.

Features:

  • Consists of two phases, Map Phase and Reduce Phase.
  • Processes big data faster with multiples nodes working under one CPU
  • YARN (yet another Resources Negotiator):

It is the resource management unit of the Hadoop framework. The data which is stored can be processed with help of YARN using data processing engines like interactive processing. It can be used to fetch any sort of data analysis.

Features:

  • It is a filing system that acts as an Operating System for the data stored on HDFS
  • It helps to schedule the tasks to avoid overloading any system

 

Advantages of the Hadoop framework

hadoop framework 2

Hadoop framework has become the most used tool to handle big data because of the various benefits that it offers.

  • Data Locality:

The concept is rather simple. The pool of data is very large, and it would be very slow and tiresome to move the data to the computation logic. By using, if data locality, the computation logic can instead be moved toward the data. This makes processing much faster.

  • Faster Data Processing:

As we saw earlier, the data is stored in small blocks using the HDFS filing system. This makes it possible to process the data parallelly using the common CPU with the help of MapReduce. This makes the performance level very high when compared to any traditional system.

  • Inbuilt fault tolerance:

The problem with using smaller cluster computers is that the risk of them crashing is very real. This is solved with the help of a high fault tolerance level that is inbuilt into the Hadoop platform. This is because of the various DataNodes that are present. This, along with parallel data processing and storage, ensures that data is available in multiple nodes, which ensures that these systems can take over and provide cover for any system that crashes. Hadoop in fact makes three copies of each file block. This ensures that any fault in the system is tolerated.

  • High Availability:

This refers to the high and easy availability of data on the Hadoop cluster. Due to the high fault tolerance that is inbuilt, the data is reliable, easily available, and can be accessed easily. Processed data can be easily accessed using YARN as well.

  • Highly Scalable:

This basically refers to the flexibility one has in scaling up or down the machines, or nodes, used for data processing. Since multiple machines are used parallelly under the same CPU, this is possible. Scaling is done according to changes in the volume of data or the requirements of the organization.

  • Flexibility:

Hadoop framework is written in Java and C, it can be easily run on any system. Further, it can be curated to suit the specific needs of the type of data. It can handle both structured and unstructured data efficiently. It can handle very different kinds of data sets, ranging from social media analysis to data warehousing.

  • Open Source:

It means it is free to use. Since it is an open-source project, the source code is available online for anyone to make modifications to. This allows the Hadoop software to be curated according to very specific needs.

  • Easy to Use:

Hadoop is easy to use since the developers need not worry about any of the processing work since it is managed by Hadoop itself. The Hadoop ecosystem is also very large and comes up with lots of tools like Hive, Pig, Spark, HBase, Mahout, etc.

  • Cost-Effective:

Not only is it highly efficient and customizable, but it also reduces the cost of processing such data significantly. Traditional data processing would require investments in very large server systems for a less efficient model. This framework instead employs cheaper investment systems to deliver a very efficient system. This makes it highly preferred by organizations.

 

Conclusion

For a reason, the Hadoop framework has grown to be one of the most popular frameworks for managing massive data. The Hadoop platform and its application framework have improved the effectiveness and efficiency of large data analysis. It is quickly rising to the top of recruiters' lists of desired skill sets and will soon be a prerequisite for data scientists.

Big Data Hadoop Certification Training Course

It would be prudent to get a certification for the same if you are looking to up-skill and are working with data science or big data analytics. Taking the help of a recognized training organization like Sprintzeal will help you a great deal in this regard. Wait no more, take the help of Sprintzeal, and get certified in Hadoop now!

 

Popular Big Data and Hadoop Courses:

Big Data Hadoop Certification Training Course

Big Data Analytics Training Course

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Akssar

Akssar

A law graduate with an immense passion for research and writing. Loves to travel, read and eat. When not doing that, loves working toward bringing well-researched and informative content to readers. Has experience in, and, is passionate about journalistic pieces, blog posts, review articles, sports coverage, technical research pieces, script-writing, website content, social media marketing, advertising, and creative writing. Sleeps when the ink runs out writing all that.

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